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TCP Traffic Classification Using Relaxed Constraints Support Vector Machines

  • Mostafa SabzekarEmail author
  • Mohammad Hossein Yaghmaee Moghaddam
  • Mahmoud Naghibzadeh

Abstract

The traffic classification problem is critical for management, security monitoring, and traffic engineering in computer networks. It has recently taken into consideration by both network operators and researchers. It allows network operators to predict future traffics and detect anomalous behavior and also allows researchers to create traffic models. In this paper, we use a new architecture of support vector machines, namely relaxed constraints support vector machines (RSVMs), to present a traffic classifier that can achieve a high accuracy without any source or destination address or port information. We just use packet length to predict the application class for each flow. RSVM is an efficient and noise-aware implementation of support vector machines that assigns an importance degree to each training sample in such a manner that noisy samples and outliers are given a less degree of importance. Experimental results with UNIBS and AUCKLAND, two sets of traffic traces coming from different topological points in the Internet, show that the proposed classifier is more reliable and has better accuracy.

Keywords

Traffic classification Support vector machines Relaxed constraints 

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References

  1. 1.
    Baker, F., Foster, B., Sharp, C.: Cisco architecture for lawful intercept in IP networks. Internet Engineering Task Force, RFC 3924 (2004)Google Scholar
  2. 2.
    Yuan, R., Li, Z., Guan, X., Xu, L.: An SVM based machine learning method for accurate internet traffic classification. Information Systems Frontiers 12(2), 149–156 (2010)CrossRefGoogle Scholar
  3. 3.
    Este, A., Gringoli, F., Salgarelli, L.: Support Vector Machines for TCP traffic classification. Computer Networks 53, 2476–2490 (2009)zbMATHCrossRefGoogle Scholar
  4. 4.
    Nguyen, T., Grenville, A.: A Survey of Techniques for Internet Traffic Classification using Machine Learning. IEEE Communications Surveys & Tutorials 10(4), 56–76 (2008)CrossRefGoogle Scholar
  5. 5.
    Internet Assigned Numbers Authority, IANA (2008), http://www.iana.org/assignments/port-numbers
  6. 6.
    Carela-Español, V., Barlet-Ros, P., Cabellos-Aparicio, A., Solé-Pareta, J.: Analysis of the impact of sampling on NetFlow traffic classification. Computer Networks 55, 1083–1099 (2011)CrossRefGoogle Scholar
  7. 7.
    Karagiannis, T., Broido, A., Faloutsos, M.: Transport layer identification of P2P traffic. In: Proceedings of ACM SIGCOMM IMC (2004)Google Scholar
  8. 8.
    Sen, S., Spatscheck, O., Wang, D.: Accurate, scalable in network identification of P2P traffic using application signatures. In: WWW 2004, New York (2004)Google Scholar
  9. 9.
    Moore, A., Papagiannaki, K.: Toward the accurate identification of network applications. In: Proc. Passive and Active Measurement Workshop (2005)Google Scholar
  10. 10.
    Williams, N., Zander, S., Armitage, G.: A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. ACM SIGCOMM Comput. Commun. Rev. 36(5) (2006)Google Scholar
  11. 11.
    Erman, J., Mahanti, A., Arlitt, M., Cohen, I., Williamson, C.: Offline/realtime traffic classification using semi-supervised learning. Performance Evaluation 64, 9–12 (2007)CrossRefGoogle Scholar
  12. 12.
    Auld, T., Moore, A., Gull, S.: Bayesian Neural Networks for Internet Traffic Classification. IEEE Transactions on Neural Networks 18(1), 223–239 (2007)CrossRefGoogle Scholar
  13. 13.
    Vapnik, V.: Statistical Learning Theory. In: Adaptive and Learning Systems for Signal Processing. Communications, and Control. Wiley, New York (1998)Google Scholar
  14. 14.
    Moore, A., Zuev, D.: Internet traffic classification using Bayesian analysis techniques. Performance Evaluation Review 33, 50–60 (2005)CrossRefGoogle Scholar
  15. 15.
    Li, Z., Yuan, R., Guan, X.: Accurate classification of the internet traffic based on the SVM method. In: International Conference on Communications, pp. 1373–1378 (2007)Google Scholar
  16. 16.
    Este, A., Gringoli, F., Salgarelli, L.: Support Vector Machines for TCP traffic classification. Computer Networks 53, 2476–2490 (2009)zbMATHCrossRefGoogle Scholar
  17. 17.
    Crotti, M., Dusi, M., Gringoli, F., Salgarelli, L.: Traffic classification through simple statistical fingerprinting. ACM SIGCOMM Computer Communication Review 37(1), 5–16 (2007)CrossRefGoogle Scholar
  18. 18.
    Sabzekar, M., Sadoghi Yazdi, H., Naghibzadeh, M.: Relaxed constraints support vector machine. Expert Systems (2011), doi:10.1111/j.1468-0394.2011.00611.xGoogle Scholar
  19. 19.
    Liu, P., Chen, P., Jiang, Q., Li, N.: Short-term traffic flow prediction based on rough set and support vector machine. In: International Conference on Fuzzy Systems and Knowledge Discovery, pp. 1526–1530 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mostafa Sabzekar
    • 1
    Email author
  • Mohammad Hossein Yaghmaee Moghaddam
    • 1
  • Mahmoud Naghibzadeh
    • 1
  1. 1.Department of Computer EngineeringFerdowsi University of MashhadMashhadIran

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